Editorial: Special Section on Reinforcement Learning and Approximate Dynamic Programming

Editorial: Special Section on Reinforcement Learning and Approximate Dynamic Programming

Approximate dynamic programming (ADP) is to compute near-optimal solutions to Markov decision problems (MDPs) with large or continuous spaces. In recent years, the research works on ADP have been brought together with the reinforcement learning (RL) community [1-4]. RL is a machine learning framework for solving sequential decision making problems that can also be modeled as the MDP formalism. ...

Approximate Dynamic Programming and Reinforcement Learning

Dynamic programming (DP) and reinforcement learning (RL) can be used to address problems from a variety of fields, including automatic control, artificial intelligence, operations research, and economy. Many problems in these fields are described by continuous variables, whereas DP and RL can find exact solutions only in the discrete case. Therefore, approximation is essential in practical DP a...

Guest Editorial: Special Issue on Adaptive Dynamic Programming and Reinforcement Learning in Feedback Control

W E are extremely pleased to present this special issue of the IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS, PART B. The importance of adaptive dynamic programming (ADP) to feedback control engineers is that it affords a methodology for learning optimal control actions online in real time based on system performance without necessarily knowing the system dynamics. When such knowledge is r...

Guest Editorial: Special Section on Learning Analytics

Reinforcement Learning And Approximate Dynamic Programming For Feedback Control

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